Research

There is an abundance of publicly available data about various biological systems, but it can be difficult to draw insight from individual datasets. Our lab develops algorithms that integrate these data to help model and understand complex biological systems. Doing this allows us to investigate many different biological conditions, including those with limited data, such as rare diseases. We recognize that our lab won’t have all the answers, or even all of the questions, so we aim to develop tools and processes that any biologist can reuse. Our approach to research prioritizes transparency, rigor, and reproducibility.

The citations on this page were generated automatically from just identifiers using the Manubot cite utility developed right here in the Greene Lab!

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2025

Characterizing substructure via mixture modeling in large-scale genetic summary statistics
Characterizing substructure via mixture modeling in large-scale genetic summary statistics
Hayley R. Stoneman, Adelle M. Price, Nikole Scribner Trout, Riley Lamont, Souha Tifour, …, Meng Lin, Nicholas Rafaels, Christopher R. Gignoux, Katie M. Marker, Audrey E. Hendricks
The American Journal of Human Genetics  ·  01 Feb 2025  ·  doi:10.1016/j.ajhg.2024.12.007

2024

Prevalence of Pathogenic Transthyretin Gene Variants in the Rocky Mountain Region
Prevalence of Pathogenic Transthyretin Gene Variants in the Rocky Mountain Region
Ellie Jacoby, Dianna Quan, Emily Todd, Jonathan Shortt, Harry Smith, Nicholas Rafaels, Kristy Crooks
Muscle & Nerve  ·  10 Dec 2024  ·  doi:10.1002/mus.28301

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